1. Technical Field
The present invention relates to computer aided diagnosis of disease, and more particularly to a system and method for sparse collaborative computer aided diagnosis.
2. Discussion of Related Art
Computer-aided diagnosis (CAD) systems have moved from the sole realm of academic publications, to robust commercial systems that are used by physicians in their clinical practice. In many CAD applications, the goal is to detect potentially malignant tumors and lesions in medical images. It is well recognized that CAD systems decrease detection and recognition errors as a second reader and reduce mistakes related to misinterpretation.
CAD systems typically focus on the diagnosis of a single isolated disease using images taken only for the specific disease. This approach neglects certain aspects of typical diagnosis procedures where physicians examine primary symptoms and tests of the disease in conjunction with other related information, such as symptoms of clinically-related conditions, patient history of other diseases and medical knowledge of highly correlated diseases.
The collaborative learning problem may be cast as a multi-task learning, a collaborative filtering or a collaborative prediction problem, depending on an application. Multi-task learning is able to capture the dependencies among tasks when several related learning problems are available. It is important to define task relatedness among tasks. A common hidden structure for all related tasks is assumed. One way to capture the task relatedness is through hierarchical Bayesian models. From the hierarchical Bayesian viewpoint, multi-task learning is essentially trying to learn a good prior over all tasks to capture task dependencies.
To tackle a CAD task, experimental features may be used to describe the potential cancerous structures or abnormal structures. This introduces irrelevant features or redundant features to the detection or classification problems. Feature selection has been an indispensable and challenging problem in this domain. Moreover, multiple tasks may be given that are related from the physical and medical perspectives are given with a limited sample size for each. Further, acquisition of medical data is expensive and time-consuming. For example, in the nodule and GGO detection tasks, often only around 100 patients are available.
Therefore, a need exists for a system and method of collaborative computer diagnosis including selecting significant features that are relevant to multiple tasks.